Manager, ML Engineer (Data Science), AI Scaling and Transformation, Engineering, AI & Data, Technology & Transformation

Do you want to be at the heart of AI & Data programmes that help public safety, justice and security organisations make better, faster and more trusted decisions? Technology & Transformation: across Security & Justice, organisations are operating in complex, highly regulated environments where operational resilience, public trust, data protection and responsible innovation are critical. That is exactly what you will help our clients to address.

You will apply machine learning, data science and advanced analytics to solve complex operational challenges, improve decision-making and support transformation in secure environments where quality, assurance and governance matter. Your work will help clients move from data science experimentation to production-ready AI and ML solutions that are scalable, explainable, secure and aligned to measurable mission outcomes.

We are seeking to hire experienced ML Engineers within our AI Scaling and Transformation team, a centre of excellence in Deloitte's Engineering, AI & Data service offering. You will work alongside clients, third parties and Deloitte teams across the Firm, supporting Security & Justice organisations to design, build and scale AI and Generative AI solutions. This is a client-facing delivery role where you will combine technical depth with strong stakeholder management, taking ownership of workstreams and translating business needs into robust, operational AI and ML capabilities.

As an ML Engineer you will play a key role in designing, developing, and delivering machine learning solutions for our clients. In this role, you may be expected to:

• Own the delivery of machine learning and data science workstreams, ensuring outputs are aligned to client priorities, delivery plans and quality standards.
• Work with client stakeholders, product owners and technical teams to understand operational challenges and translate them into practical ML solution designs.
• Design, build, test and deploy machine learning models, analytical pipelines and data science components that are robust, scalable and maintainable.
• Support the operationalisation of ML solutions through MLOps practices, including model monitoring, versioning, automated testing, CI/CD and performance optimisation.
• Apply responsible AI, model explainability, security, privacy and governance considerations throughout the development lifecycle, particularly within secure and regulated environments.
• Collaborate with solution architects, data engineers, data scientists, business analysts and delivery leads to integrate ML solutions into wider technology and business landscapes.
• Manage stakeholders and contribute to delivery leadership by communicating progress, risks, dependencies and technical recommendations clearly and confidently.

We are looking for candidates who are able to demonstrate skills and experience in some of the following:

• Degree or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence, Machine Learning or a related discipline.
• Hands-on experience designing, developing and deploying machine learning or data science solutions in a consulting, commercial, public sector or technology delivery environment.
• Experience owning technical deliverables or workstreams, managing priorities and coordinating across multidisciplinary teams.
• Demonstrated ability to work with business and technical stakeholders to understand requirements, shape solution options and translate analytical outputs into practical recommendations.
• Experience delivering within secure, regulated or complex public sector environments is advantageous; candidates must be eligible and willing to obtain or hold the required security clearance for relevant client engagements.

Strong proficiency in Python or another modern programming language used for data science and machine learning development. Practical experience with machine learning frameworks and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow or equivalent. Experience across the end-to-end ML lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring and optimisation. Understanding of MLOps and production ML practices, including CI/CD, model versioning, automated testing, retraining, monitoring and reproducible delivery. Experience using cloud platforms and data science environments such as Azure, AWS, GCP, Databricks or equivalent to build and deploy scalable ML solutions. Strong communication, stakeholder management and problem-solving skills, with the ability to explain complex modelling approaches, trade-offs and outcomes to technical and non-technical audiences.

If you want to help solve some of the biggest tech and transformational challenges around, join us. Together, we'll make an impact that matters.